Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/13613
Title: Modelling of dengue fever and its vector risks based on the impacts of socioeconomic, meteorological and environmental factors: a Geographic Information System-based case study of Jeddah, Saudi Arabia
Contributor(s): Khormi, Hassan (author); Kumar, Lalit  (supervisor)orcid ; Elzahrany, Ramze (supervisor)
Conferred Date: 2013
Copyright Date: 2013
Open Access: Yes
Handle Link: https://hdl.handle.net/1959.11/13613
Abstract: The incidence of dengue fever (DF) and its vector ('Aedes aegypti') is largely associated with environmental, meteorological and socioeconomic conditions. DF is considered by the World Health organization (WHO) to be one of the most important mosquito-borne diseases globally. It has been recognised as the most prevalent arboviral disease in Saudi Arabia generally and Jeddah particularly. The transmission pattern of DF is sensitive to socio ecological factors including environmental variation, socioeconomic variables and mosquito density. The aim of this study was mainly to model and develop models for dengue fever risks based on different environmental, meteorological and socioeconomic variables and to assess the association between these variables, DF cases and 'Aedes aegypti' in a geographic information system (GIS) environment. Different variables, including clinically confirmed cases of DF, mosquito counts, meteorological factors (e.g. temperature and relative humidity), population density based on inhabited areas, total population per district, neighbourhood quality, subsurface water and monthly spatio-temporal risk of DF based on average weekly frequency of DF incidence have been used. A number of spatial, analytical and descriptive methods for gathering, analysing and modelling data were used in this study, such as Getis-Ord Gi*, multi-distance spatial cluster (Ripley's K-function), frequency index, derivation of socioeconomic factors from high-resolution satellite images, Spearman's and Person's correlations, multiple regression analysis and geographically weighted regression (GWR).
Publication Type: Thesis Doctoral
Field of Research Codes: 111711 Health Information Systems (incl Surveillance)
Socio-Economic Outcome Codes: 920407 Health Protection and/or Disaster Response
Rights Statement: Copyright 2013 - Hassan Muhsan A Khormi
HERDC Category Description: T2 Thesis - Doctorate by Research
Other Links: http://www.geospatialhealth.unina.it/summary.php?ida=157
http://www.geospatialhealth.unina.it/summary.php?ida=142
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Appears in Collections:School of Environmental and Rural Science
Thesis Doctoral

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